Occupational mortality studies have traditionally been plagued by bias resulting from improper comparisons of working populations with the general population. For example, the mortality rate for ASCVD in working populations is usually 60%-90% of the general population rate. Thus, the general population cannot serve as an appropriate control group for detecting relative risks in the range 1.3-2.5. Since present day exposures are often lower than past exposures, detection of relative risks less than 2.5 has become Grant=R01ES03405 Occupational mortality studies have traditionally been plagued by bias resulting from improper comparisons of working populations with the general population. For example, the mortality rate for ASCVD in working populations is usually 60%-90% of the general population rate. Thus, the general population cannot serve as an appropriate control group for detecting relative risks in the range 1.3-2.5. Since present day exposures are often lower than past exposures, detection of relative risks less than 2.5 has become of increased concern. Therefore, epidemiologists have increasingly relied upon intracohort (i.e., internal) comparisons. Unfortunately, when workers at increased risk terminate employment early, date of termination of employment is both a risk factor for death and a determinate employment early, date of termination of employment is both a risk factor for death and a determinant of subsequent exposure. Therefore, standard intracohort methods that estimate mortality as a function of cumulative exposure can underestimate the true effect of exposure, whether or not one adjusts for time of termination of employment [1-7]. Thus, even in intracohort analyses, relative risks of 1.3-2.5 can be masked by the early termination of workers at elevated risk. A principal aim of this grant is further development and implementation of new methods to control this bias based on the "G-null test algorithm", the "Monte-Carlo G-computation algorithm" and the "G- estimation" algorithm. The "G-null test algorithm" is a distribution- free test of the null hypothesis of no casual effects of exposure. The other algorithms are new semiparametric procedures for estimating the magnitude of any exposure effect. The ASCVD, nonmalignant respiratory disease, and/or lung cancer mortality in cohorts of arsenic-exposed copper smelter workers, solvent-exposed rubber workers, formaldehyde- exposed chemical workers, cobalt-exposed hard metal workers. Canadian asbestos miners, U.S. uranium miners, cutting oil-exposed auto workers, and nitroglycerin-exposed munitions workers will be reanalized with the new methods and with standard methods, and the results compared. These analytic methods may be necessary to control bias in nonoccupational studies in which a risk factor for the outcome under study determines subsequent exposure to the study agent. These methods will be used to examine the effect of cigarette smoking on pulmonary function in the Harvard Six Cities study. These methods are necessary to control bias because current level of pulmonary function is a determinant of both subsequent lung function and smoking behavior (since subjects with poor pulmonary function are more likely to quit smoking).